Purpose
The purpose of this notebook is to use the raw travel time data to experiment with different methods of aggregation and score modeling.
Import libraries
library(tidyverse)
library(ggplot2)
# For pretty knitting
library(lemon)
knit_print.data.frame <- lemon_print
knit_print.tbl <- lemon_print
knit_print.summary <- lemon_print
Import Scoring Functions
source('Score_Functions.R')
# normalize_vec(vec, x=0.01, y=0.99, log = FALSE)
# normalize_df(df, x = 0.01, y = 0.99, log = FALSE)
# sum score function : SUM [i..n] (1 / (traveltime_i * std_traveltime_i) + ... ))
# sum_score_fxn(df, weight = FALSE, log_normalize_score = TRUE, normalize_df = FALSE, x=1, y=10)
# function to plot score distributions by type
plot_densities <- function(score_frame1, score_frame2, titl1, titl2) {
x <- score_frame1 %>%
ggplot(aes(x = score, color = type)) +
geom_density() +
egg::theme_article() +
theme(aspect.ratio = 0.3) +
ggtitle(titl1)
y <- score_frame2 %>%
ggplot(aes(x = score, color = type)) +
geom_density() +
egg::theme_article() +
theme(aspect.ratio = 0.3)+
ggtitle(titl2)
gridExtra::grid.arrange(x, y)
}
Import data
## Import raw Travel Time Matrix (ttm)
ttm <- read.csv('../../data/clean/ttm.csv')
n_origins <- 15197 # known origins
n_amenities <- 346 # known destinations from considered amenities
paste('Origins considered:', round(length(unique(ttm$fromId))/n_origins*100, 2), '%')
[1] "Origins considered: 94.44 %"
paste('Destinations considered:', round(length(unique(ttm$toId))/n_amenities*100, 2), '%')
[1] "Destinations considered: 124.57 %"
paste('Rows = ', nrow(ttm))
[1] "Rows = 5162695"
# convert Ids from double to factor
ttm$fromId <- as.factor(ttm$fromId)
ttm$toId <- as.factor(ttm$toId)
summary(ttm[,3:4])
avg_unique_time sd_unique_time
Min. : 0.00 Min. : 0.1601
1st Qu.: 52.54 1st Qu.: 1.9428
Median : 72.18 Median : 2.8868
Mean : 72.79 Mean : 3.4044
3rd Qu.: 94.21 3rd Qu.: 4.3813
Max. :119.00 Max. :35.3553
Data Wrangling
Wrangling Notes: - Remove skews and extreme values - Due to the diversity in amenity types (which all serve a unique cultural purpose), we’ll consider them independently for accessibility score computations. - Amenities which were interested in studying have already been filtered out in the ttm computation. They are the following: - Museums - Libraries - Galleries - Theatres
Import and join amenity types
target_amenities <- c('gallery', 'museum', 'library or archives', 'theatre/performance and concert hall')
amenities <- read.csv('../../data/clean/vancouver_facilities_2.csv') %>% filter(type %in% target_amenities)
# preview original
sample_n(amenities, 3)
# clean
amenities <- amenities[,c(1,4)] # only need id and type columns
amenities$id <- as.factor(amenities$id) # convert to factor
amenities$type <- as.factor(amenities$type) # convert to factor
# preview clean
sample_n(amenities, 3)
# view summary
amenities %>% group_by(type) %>% summarise(count = n()) %>% arrange(desc(count))
ttm <- ttm %>% left_join(amenities, by = c('toId' = 'id'))
names(ttm)[names(ttm) == 'avg_unique_time'] <- "avg_time"
names(ttm)[names(ttm) == 'sd_unique_time'] <- "sd_time"
summary(ttm[,3:4])
avg_time sd_time
Min. : 0.00 Min. : 0.1601
1st Qu.: 52.54 1st Qu.: 1.9428
Median : 72.18 Median : 2.8868
Mean : 72.79 Mean : 3.4044
3rd Qu.: 94.21 3rd Qu.: 4.3813
Max. :119.00 Max. :35.3553
sample_n(ttm, 5)
par(mfrow = c(1,2))
plot(density(ttm[,3]), main = 'Travel Time (Density)')
plot(density(ttm[,4]), main = 'Std Dev of Travel Time (Density)')

Replace travel times less than 5 minutes to 5 minutes
This is done to prevent infinity values in the scoring. Normalization will be done to prevent zero values but it still creates a largely skewed score if we include travel times that approach zero. 5 minutes is also a realistic time window for any travel time that may take 0 - 5 minutes.
par(mfrow = c(1, 2))
hist((ttm$avg_time), xlab = 'Original Travel Time', main = '',
xlim = c(0, 25), ylim = c(0, 120000))
# set travel times <5 minutes to 5 minutes
min_5min <- pmax(ttm$avg_time, 5)
hist(min_5min, xlab = 'Original Travel Time', main = '',
xlim = c(0, 25), ylim = c(0, 120000))

ttm$avg_time <- min_5min
Correct skew in standard deviation
This will be important to prevent skew amplification in the score computation.
# correct the skew in addition to edges close to zero
temp <- log(ttm$sd_time + 1) # +1 just prevents zero values
plot(density(temp), main = 'Log+1 Standard Deviation Density', xlim = c(0,4))

# set sd_unique_time to be the Log+1 corrected values
ttm$sd_time <- temp
Add Amenity Weights
# Import weight
dest_wts <- read.csv('../../data/amenity_score/poi_index.csv')
# clean
dest_wts <- dest_wts[, c(6,7)] # keep weight, id
names(dest_wts) <- c('weight', 'id')
dest_wts$id <- as.factor(dest_wts$id)
head(dest_wts)
# see weight distribution
plot(density(dest_wts$weight), main = 'Amenity Popularity Distribution')

# join column
ttm_wts <- left_join(ttm, dest_wts, by = c('toId'='id'))
# If any weights are undefined replace with 1
ttm_wts$weight[is.na(ttm_wts$weight)] <- 1
head(ttm_wts)
NA
NA
Sum Scoring Method
# scores with [1 - 100] df normalization
na.omit(ttm_wts)->ttm_wts
ttm_scores <- sum_score_fxn(ttm_wts,
weight = FALSE,
log_normalize_score = TRUE,
normalize_df = TRUE, x = 1, y = 100)
ttm_wtd_scores <- sum_score_fxn(ttm_wts,
weight = TRUE,
log_normalize_score = TRUE,
normalize_df = TRUE, x = 1, y = 100)
Sum Scoring Method 2 with mean plus sd
ttm_scores_2 <- sum_score_fxn_2(ttm_wts,
weight = FALSE,
log_normalize_score = TRUE,
normalize_df = TRUE, x = 1, y = 100)
ttm_wtd_scores_2 <- sum_score_fxn_2(ttm_wts,
weight = TRUE,
log_normalize_score = TRUE,
normalize_df = TRUE, x = 1, y = 100)

# scores with [0.01 - 0.99] df normalization
ttm_scores2 <- sum_score_fxn(ttm_wts,
weight = FALSE,
log_normalize_score = TRUE,
normalize_df = TRUE, x = 0.01, y = 0.99)
ttm_wtd_scores2 <- sum_score_fxn(ttm_wts,
weight = TRUE,
log_normalize_score = TRUE,
normalize_df = TRUE, x = 0.01, y = 0.99)
plot_densities(ttm_scores2, ttm_wtd_scores2, 'Unweighted Scores', 'Weighted Scores')
Sum Scoring for the Nearest 1, 2, or 3 Amenities
Note that for nearest 1, the sum is the value itself.
# Keep only the nearest 1, 2, or 3 travel times for each dissemination block
nearest_1_ttm <- ttm_wts %>%
group_by(fromId, type) %>%
summarise(avg_time = min(avg_time),
sd_time = sd_time[which.min(avg_time)],
weight = weight[which.min(avg_time)])
nearest_2_ttm <- ttm_wts %>%
group_by(fromId, type) %>%
summarise(avg_time = na.omit(sort(avg_time)[1:2]),
sd_time = sd_time[which(na.omit(avg_time == sort(avg_time)[1:2]))],
weight = weight[which(na.omit(avg_time == sort(avg_time)[1:2]))])
nearest_3_ttm <- ttm_wts %>%
group_by(fromId, type) %>%
summarise(avg_time = na.omit(sort(avg_time)[1:3]),
sd_time = sd_time[which(na.omit(avg_time == sort(avg_time)[1:3]))],
weight = weight[which(na.omit(avg_time == sort(avg_time)[1:3]))])
# scores by nearest amenities
n1_ttm_score <- sum_score_fxn(nearest_1_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n1_wt_ttm_score <- sum_score_fxn(nearest_1_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n2_ttm_score <- sum_score_fxn(nearest_2_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n2_wt_ttm_score <- sum_score_fxn(nearest_2_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n3_ttm_score <- sum_score_fxn(nearest_3_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n3_wt_ttm_score <- sum_score_fxn(nearest_3_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
plot_densities(n1_ttm_score, n1_wt_ttm_score, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')

plot_densities(n2_ttm_score, n2_wt_ttm_score, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')

plot_densities(n3_ttm_score, n3_wt_ttm_score, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')

using score function of 1/(mean+2sd)
# scores by nearest amenities
n1_ttm_score_2 <- sum_score_fxn_2(nearest_1_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
`summarise()` has grouped output by 'fromId'. You can override using the `.groups` argument.
n1_wt_ttm_score_2 <- sum_score_fxn_2(nearest_1_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
`summarise()` has grouped output by 'fromId'. You can override using the `.groups` argument.
n2_ttm_score_2 <- sum_score_fxn_2(nearest_2_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
`summarise()` has grouped output by 'fromId'. You can override using the `.groups` argument.
n2_wt_ttm_score_2 <- sum_score_fxn_2(nearest_2_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
`summarise()` has grouped output by 'fromId'. You can override using the `.groups` argument.
n3_ttm_score_2 <- sum_score_fxn_2(nearest_3_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
`summarise()` has grouped output by 'fromId'. You can override using the `.groups` argument.
n3_wt_ttm_score_2 <- sum_score_fxn(nearest_3_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
`summarise()` has grouped output by 'fromId'. You can override using the `.groups` argument.
plot_densities(n1_ttm_score_2, n1_wt_ttm_score_2, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')

plot_densities(n2_ttm_score_2, n2_wt_ttm_score_2, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')

plot_densities(n3_ttm_score_2, n3_wt_ttm_score_2, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')

Exporting all Score Sets
## Add weight column for each score frame
ttm_scores$weight <- as.factor('no')
ttm_wtd_scores$weight <- as.factor('yes')
n1_ttm_score$weight <- as.factor('no')
n1_wt_ttm_score$weight <- as.factor('yes')
n2_ttm_score$weight <- as.factor('no')
n2_wt_ttm_score$weight <- as.factor('yes')
n3_ttm_score$weight <- as.factor('no')
n3_wt_ttm_score$weight <- as.factor('yes')
## Add nearest_n column for each score frame
ttm_scores$nearest_n <- as.factor('all')
ttm_wtd_scores$nearest_n <- as.factor('all')
n1_ttm_score$nearest_n <- as.factor('1')
n1_wt_ttm_score$nearest_n <- as.factor('1')
n2_ttm_score$nearest_n <- as.factor('2')
n2_wt_ttm_score$nearest_n <- as.factor('2')
n3_ttm_score$nearest_n <- as.factor('3')
n3_wt_ttm_score$nearest_n <- as.factor('3')
## Combine into a long dataframe
all_scores <- list(ttm_scores, ttm_wtd_scores,
n1_ttm_score, n1_wt_ttm_score,
n2_ttm_score, n2_wt_ttm_score,
n3_ttm_score, n3_wt_ttm_score)
long_scores <- data.table::rbindlist(all_scores) %>% arrange(fromId)
## Re-Order columns
long_scores <- long_scores[, c(1, 2, 4, 5, 3)]
## Export
write.csv(long_scores, '../../data/score_sets/long_scores.csv', row.names = FALSE)
---
title: "Score Computation Notebook"
output:
  pdf_document: default
  html_notebook: default
  html_document:
    df_print: paged
---

# Purpose

The purpose of this notebook is to use the raw travel time data to experiment with different methods of aggregation and score modeling.

## Import libraries
```{r message=FALSE, warning=FALSE}
library(tidyverse)
library(ggplot2)

# For pretty knitting
library(lemon)
knit_print.data.frame <- lemon_print
knit_print.tbl <- lemon_print
knit_print.summary <- lemon_print


```

## Import Scoring Functions
```{r}
source('Score_Functions.R')

# normalize_vec(vec, x=0.01, y=0.99, log = FALSE)
# normalize_df(df, x = 0.01, y = 0.99, log = FALSE)

# sum score function : SUM [i..n] (1 / (traveltime_i * std_traveltime_i) + ... ))
# sum_score_fxn(df, weight = FALSE, log_normalize_score = TRUE, normalize_df = FALSE, x=1, y=10)

```

```{r}
# function to plot score distributions by type
plot_densities <- function(score_frame1, score_frame2, titl1, titl2) {
  x <- score_frame1 %>%
        ggplot(aes(x = score, color = type)) +
        geom_density() +
        egg::theme_article() +
        theme(aspect.ratio = 0.3) +
        ggtitle(titl1)
  y <- score_frame2 %>%
        ggplot(aes(x = score, color = type)) +
        geom_density() +
        egg::theme_article() +
        theme(aspect.ratio = 0.3)+
        ggtitle(titl2)
  gridExtra::grid.arrange(x, y)
}

```


## Import data 
```{r kable.opts=list(caption='Summary Table')}

## Import raw Travel Time Matrix (ttm)
ttm <- read.csv('../../data/clean/ttm.csv')

n_origins <- 15197 # known origins
n_amenities <- 346 # known destinations from considered amenities

paste('Origins considered:', round(length(unique(ttm$fromId))/n_origins*100, 2), '%')
paste('Destinations considered:', round(length(unique(ttm$toId))/n_amenities*100, 2), '%')
paste('Rows = ', nrow(ttm))

# convert Ids from double to factor
ttm$fromId <- as.factor(ttm$fromId)
ttm$toId <- as.factor(ttm$toId)

summary(ttm[,3:4])
```

## Data Wrangling 

**Wrangling Notes:**
- Remove skews and extreme values
- Due to the diversity in amenity types (which all serve a unique cultural purpose), we'll consider them independently for accessibility score computations.
- Amenities which were interested in studying have already been filtered out in the ttm computation. They are the following:
  - Museums
  - Libraries
  - Galleries
  - Theatres

**Import and join amenity types**

```{r kable.opts=list(caption='Summary Table')}

target_amenities <- c('gallery', 'museum', 'library or archives', 'theatre/performance and concert hall')
amenities <- read.csv('../../data/clean/vancouver_facilities_2.csv') %>% filter(type %in% target_amenities)

# preview original
sample_n(amenities, 3)

# clean
amenities <- amenities[,c(1,4)] # only need id and type columns
amenities$id <- as.factor(amenities$id)     # convert to factor
amenities$type <- as.factor(amenities$type) # convert to factor

# preview clean
sample_n(amenities, 3)

# view summary
amenities %>% group_by(type) %>% summarise(count = n()) %>% arrange(desc(count))

ttm <- ttm %>% left_join(amenities, by = c('toId' = 'id'))

names(ttm)[names(ttm) == 'avg_unique_time'] <- "avg_time"
names(ttm)[names(ttm) == 'sd_unique_time'] <- "sd_time"

summary(ttm[,3:4])
sample_n(ttm, 5)

par(mfrow = c(1,2))
plot(density(ttm[,3]), main = 'Travel Time (Density)')
plot(density(ttm[,4]), main = 'Std Dev of Travel Time (Density)')
```


**Replace travel times less than 5 minutes to 5 minutes**

This is done to prevent infinity values in the scoring. Normalization will be done to prevent zero values but it still creates a largely skewed score if we include travel times that approach zero. 5 minutes is also a realistic time window for any travel time that may take 0 - 5 minutes.

```{r}
par(mfrow = c(1, 2))

hist((ttm$avg_time), xlab = 'Original Travel Time', main = '',
     xlim = c(0, 25), ylim = c(0, 120000))

# set travel times <5 minutes to 5 minutes
min_5min <- pmax(ttm$avg_time, 5)
hist(min_5min, xlab = 'Original Travel Time', main = '',
     xlim = c(0, 25), ylim = c(0, 120000))

ttm$avg_time <- min_5min
```

**Correct skew in standard deviation**

This will be important to prevent skew amplification in the score computation.

```{r}
# correct the skew in addition to edges close to zero

temp <- log(ttm$sd_time + 1) # +1 just prevents zero values
plot(density(temp), main = 'Log+1 Standard Deviation Density', xlim = c(0,4))

# set sd_unique_time to be the Log+1 corrected values
ttm$sd_time <- temp

```

## Add Amenity Weights

```{r kable.opts=list(caption='Summary Table')}

# Import weight
dest_wts <- read.csv('../../data/amenity_score/poi_index.csv')

# clean
dest_wts <- dest_wts[, c(6,7)] # keep weight, id
names(dest_wts) <- c('weight', 'id')
dest_wts$id <-  as.factor(dest_wts$id)
head(dest_wts)

# see weight distribution
plot(density(dest_wts$weight), main = 'Amenity Popularity Distribution')

# join column
ttm_wts <- left_join(ttm, dest_wts, by = c('toId'='id'))

# If any weights are undefined replace with 1
ttm_wts$weight[is.na(ttm_wts$weight)] <- 1

head(ttm_wts)


```

## Sum Scoring Method

```{r message=FALSE, warning=FALSE}

# scores with [1 - 100] df normalization 


na.omit(ttm_wts)->ttm_wts


ttm_scores <- sum_score_fxn(ttm_wts,
                            weight = FALSE,
                            log_normalize_score = TRUE,
                            normalize_df = TRUE, x = 1, y = 100)

ttm_wtd_scores <- sum_score_fxn(ttm_wts,
                            weight = TRUE,
                            log_normalize_score = TRUE,
                            normalize_df = TRUE, x = 1, y = 100)
```
## Sum Scoring Method 2 with mean plus sd

```{R}
ttm_scores_2 <- sum_score_fxn_2(ttm_wts,
                            weight = FALSE,
                            log_normalize_score = TRUE,
                            normalize_df = TRUE, x = 1, y = 100)

ttm_wtd_scores_2 <- sum_score_fxn_2(ttm_wts,
                            weight = TRUE,
                            log_normalize_score = TRUE,
                            normalize_df = TRUE, x = 1, y = 100)

```

```{r}
par(mfrow=c(1,4))
plot_densities(ttm_scores, ttm_wtd_scores, 'Unweighted Scores', 'Weighted Scores')
plot_densities(ttm_scores_2, ttm_wtd_scores_2, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')

```


```{r}

# scores with [0.01 - 0.99] df normalization 

ttm_scores2 <- sum_score_fxn(ttm_wts,
                            weight = FALSE,
                            log_normalize_score = TRUE,
                            normalize_df = TRUE, x = 0.01, y = 0.99)

ttm_wtd_scores2 <- sum_score_fxn(ttm_wts,
                            weight = TRUE,
                            log_normalize_score = TRUE,
                            normalize_df = TRUE, x = 0.01, y = 0.99)

plot_densities(ttm_scores2, ttm_wtd_scores2, 'Unweighted Scores', 'Weighted Scores')

```



## Sum Scoring for the Nearest 1, 2, or 3 Amenities

*Note that for nearest 1, the sum is the value itself.*

```{r message=FALSE, warning=FALSE}

# Keep only the nearest 1, 2, or 3 travel times for each dissemination block

nearest_1_ttm <- ttm_wts %>%
                  group_by(fromId, type) %>%
                  summarise(avg_time = min(avg_time), 
                            sd_time = sd_time[which.min(avg_time)],
                            weight = weight[which.min(avg_time)])

nearest_2_ttm <- ttm_wts %>%
                  group_by(fromId, type) %>%
                  summarise(avg_time = na.omit(sort(avg_time)[1:2]), 
                            sd_time = sd_time[which(na.omit(avg_time == sort(avg_time)[1:2]))], 
                            weight = weight[which(na.omit(avg_time == sort(avg_time)[1:2]))])

nearest_3_ttm <- ttm_wts %>%
                  group_by(fromId, type) %>%
                  summarise(avg_time = na.omit(sort(avg_time)[1:3]), 
                            sd_time = sd_time[which(na.omit(avg_time == sort(avg_time)[1:3]))], 
                            weight = weight[which(na.omit(avg_time == sort(avg_time)[1:3]))])


# scores by nearest amenities

n1_ttm_score <- sum_score_fxn(nearest_1_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n1_wt_ttm_score <- sum_score_fxn(nearest_1_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)

n2_ttm_score <- sum_score_fxn(nearest_2_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n2_wt_ttm_score <- sum_score_fxn(nearest_2_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)

n3_ttm_score <- sum_score_fxn(nearest_3_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n3_wt_ttm_score <- sum_score_fxn(nearest_3_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)

```

```{r}
plot_densities(n1_ttm_score, n1_wt_ttm_score, 'Unweighted Scores', 'Weighted Scores')
plot_densities(n2_ttm_score, n2_wt_ttm_score, 'Unweighted Scores', 'Weighted Scores')
plot_densities(n3_ttm_score, n3_wt_ttm_score, 'Unweighted Scores', 'Weighted Scores')

```

### using score function of 1/(mean+2sd)


```{r}
# scores by nearest amenities

n1_ttm_score_2 <- sum_score_fxn_2(nearest_1_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n1_wt_ttm_score_2 <- sum_score_fxn_2(nearest_1_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)

n2_ttm_score_2 <- sum_score_fxn_2(nearest_2_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n2_wt_ttm_score_2 <- sum_score_fxn_2(nearest_2_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)

n3_ttm_score_2 <- sum_score_fxn_2(nearest_3_ttm, weight = FALSE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)
n3_wt_ttm_score_2 <- sum_score_fxn(nearest_3_ttm, weight = TRUE, log_normalize_score = TRUE, normalize_df = TRUE, x = 1, y = 100)

```


```{r}
plot_densities(n1_ttm_score_2, n1_wt_ttm_score_2, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')
plot_densities(n2_ttm_score_2, n2_wt_ttm_score_2, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')
plot_densities(n3_ttm_score_2, n3_wt_ttm_score_2, 'Unweighted Scores with 1/(Mean + 2*Sd)', 'Weighted Scores with 1/(Mean+2*Sd)')

```

## Exporting all Score Sets

```{r}

## Add weight column for each score frame

ttm_scores$weight <- as.factor('no')
ttm_wtd_scores$weight <- as.factor('yes')

n1_ttm_score$weight <- as.factor('no')
n1_wt_ttm_score$weight <- as.factor('yes')

n2_ttm_score$weight <- as.factor('no')
n2_wt_ttm_score$weight <- as.factor('yes')

n3_ttm_score$weight <- as.factor('no')
n3_wt_ttm_score$weight <- as.factor('yes')

## Add nearest_n column for each score frame

ttm_scores$nearest_n <- as.factor('all')
ttm_wtd_scores$nearest_n <- as.factor('all')

n1_ttm_score$nearest_n <- as.factor('1')
n1_wt_ttm_score$nearest_n <- as.factor('1')

n2_ttm_score$nearest_n <- as.factor('2')
n2_wt_ttm_score$nearest_n <- as.factor('2')

n3_ttm_score$nearest_n <- as.factor('3')
n3_wt_ttm_score$nearest_n <- as.factor('3')

## Combine into a long dataframe
all_scores <- list(ttm_scores, ttm_wtd_scores,
                   n1_ttm_score, n1_wt_ttm_score,
                   n2_ttm_score, n2_wt_ttm_score,
                   n3_ttm_score, n3_wt_ttm_score)

long_scores <- data.table::rbindlist(all_scores) %>% arrange(fromId)

## Re-Order columns
long_scores <- long_scores[, c(1, 2, 4, 5, 3)]

## Export
write.csv(long_scores, '../../data/score_sets/long_scores.csv', row.names = FALSE)
```







# Old Notes ~ Ignore or reuse later

| Name | Function | Notes | Assumptions |
|---|---|---|---|
|Unweighted Naive | number of accessible points / (mean transit time * mean standard deviation in transit time)  | Mean transit time to all accessible destinations  | Assumes that accessibility is defined by access to all amenities |
|Weighted Naive | popularity weighted accessible points / (mean transit time * mean standard deviation in transit time)  | Mean transit time to all accessible destinations  | Assumes that accessibility is defined by access to all amenities and that amenity popularity defines significance of an accessible amenity |
|Unweighted Sum | 1 / (nearest amenity transit time + standard deviation in nearest transit time)  | Only considers the nearest 1 to 3 amenities of a certain category. Sum is used to prevent skewing of data (difference(1/(0.01\*0.01) and 1/(6\*6)) >>> difference(1/(0.01+0.01) and 1/(6+6))) | Assumes accessibility only defined by access to the nearest amenity type  |
| Joseph Unweighted Sum | sum(1 / (normalized_transit_time_i*normalized_sd_time_i)) | Sums the transit times as opposed to taking the mean, then normalizes the scores. |
















